impact assessment report (iar) (d5.1) - gfz-potsdam.de€¦ · cryosat elevation rate corrections...

Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica

(REGINA)

Impact Assessment Report (IAR) (D5.1)

The REGINA consortium German Research Centre for Geosciences (GFZ)

Newcastle University (NCL) TU München (IAPG)

University of Bristol (UOB) Email: [email protected]

www.regina-science.eu

ESA ITT Ref.: EOP-SA/0175/DFP-dfp Tender: AO 1-7158

Contract-Nr.: 4000107393/12/I-NB Issue: 2.2

Date: January 13, 2015 Ref.: REGINA_D5_1_Issue_2.2

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Document history: REGINA_D5_1_issue_1.0: First version sent to MD REGINA_D5_1_issue_2.0: Commented by MD REGINA_D5_1_issue_2.1: Comments by MD included REGINA_D5_1_issue_2.2: Final document for publication

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Table of contents

0 Preface ............................................................................................................. 4

1 Determination of ice sheet topographic change ............................................... 6

1.1 Comparison with existing results ................................................................................... 6

1.2 Quantification of improvement ..................................................................................... 8

1.3 Analysis of the errors / uncertainties ............................................................................. 9

2 GPS solutions in Antarctica ............................................................................... 9

2.1 Comparison with existing results ................................................................................. 10

2.2 Quantification of improvement ................................................................................... 14

3 Gravity field observations .............................................................................. 15

3.1 Comparison with existing results ................................................................................. 15

3.2 Analysis of the errors / uncertainties ........................................................................... 15

4 GIA prediction ................................................................................................ 17

5 Combined GIA estimate for Antarctica ........................................................... 17

5.1 Impact on CryoSat-2 volume rates ............................................................................... 19

5.2 Effect of GPS coverage ................................................................................................ 21

5.3 Sensitivity to filtering of gravity fields ......................................................................... 23

5.4 Impact on GRACE mass balances ................................................................................. 25

5.5 Remark on uncertainties ............................................................................................. 28

6 Summary ........................................................................................................ 30

7 References ..................................................................................................... 30

Appendix ............................................................................................................. 32

A1. Earth model parameters ............................................................................................. 32

A2. GPS time series ........................................................................................................... 33

A3. List of GPS sites ........................................................................................................... 44

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0 Preface

Purpose of this document

The project REGINA (www.regina-science.eu) funded by the Support To Science Element (STSE) of the European Space Agency (ESA) aims at improving land-elevation rate corrections for CryoSat due to glacial-isostatic adjustment (GIA) for Antarctica, employing multiple space-geodetic data and numerical modeling. This document is the Impact Assessment Report (IAR), presenting the improvements achieved by the re-processing of the data sets, and the impact of the GIA estimate on CryoSat-2 volume balances and GRACE mass balances.

Applicable documentation

In addition to published literature, the following applicable documents [AD-] are cited in this report and can be obtained upon request from the REGINA project PI:

[AD-1] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Requirements Baseline for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 1.1, Doc. Ref. REGINA_D1_1_issue_1.1, http://dep1doc.gfz-potsdam.de/documents/46, www.regina-science.eu.

[AD-2] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Dataset User Manual (D2.2) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 2.2, Doc. Ref. REGINA_D2_2_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/47, www.regina-science.eu.

[AD-3] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Algorithm Theoretical Basis Document (D3.1) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 2.0, Doc. Ref. REGINA_D3_1_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/56, www.regina-science.eu.

[AD-4] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Validation Report (D3.2) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 2.2, Doc. Ref. REGINA_D3_2_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/61, www.regina-science.eu.

[AD-5] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Impact Assessment Report (D5.1) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 2.2, Doc. Ref. REGINA_D5_1_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/62, www.regina-science.eu.

[AD-6] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Scientific Roadmap (D5.2) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections in Antarctica, Issue 2.2, Doc. Ref. REGINA_D5_2_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/69, www.regina-science.eu.

[AD-7] Sasgen, I. & the REGINA Consortium (2014): ESA ITT CryoSat+ REGINA: Final Report (D6.1) for determining Regional glacial isostatic adjustment and CryoSat elevation rate corrections

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in Antarctica, Issue 2.2, Doc. Ref. REGINA_D6_1_issue_2.2, http://dep1doc.gfz-potsdam.de/documents/70, www.regina-science.eu.

Section overview and relation to requirements baseline

Section 1 though Section 3 describe the impact of the advanced processing on the resulting trends obtained from altimetry, GPS and GRACE, respectively.

Section 4 refers to the Validation Report, where the impact of the Earth structure has been presented and compared with published literature. A selection of two end members of GIA simulations is described.

Section 5 presents the impact analysis of the GIA estimate for CryoSat-2 and GRACE data.

Section 6 summarizes the results of REGINA.

Appendix A1 lists the Earth model parameters as described in the ATBD [AD-3]. Appendix A2 shows the GPS time series the trends presented in Section 2 are based upon. Appendix A3 presents a list of location, trend, uncertainty, and other information derived for each GPS site.




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1 Determination of ice sheet topographic change

The REGINA altimetry data set is a combination of radar (Envisat) and laser altimetry (ICESat) trends over the ICESat period (02/2003-10/2009). The resolution is 10 km on a polar stereographic grid with -71°S central latitude; volume biases introduced by the distortions of the projected coordinates away from the central latitude are compensated by an area scaling factor (see Section 5.1). Trends are chosen from either data set if the point is not available in the other; if a point is available in both data sets the point with the smaller standard error is used (Section 1.4, ATBD [AD-3]). Because of the higher accuracy in laser altimetry, the majority of trends used stem from ICESat data. We include two versions of the data set, one without interpolation (missing data points set to a value of zero) and one with nearest neighbour-interpolation for missing areas (10 km grid, nearest neighbour, error weighted, search radius 150 km) to provide complete coverage of the grounded ice sheet (Wessel et al. 2013). We compare the REGINA data set with existing results, thereby focusing on volume estimates to avoid the more complex problems of firn compaction and surface mass balance (SMB) estimates which would be necessary in a comparison of mass balance estimates.

Figure 1.1: Outline of Antarctic drainage basins used in REGINA (left; Sasgen et al. 2013) and IMBIE (Shepherd et al. 2012; Smith et al. 2014). Most of the outlines agree to perform a direct comparison. Some basins need to be aggregated to achieve consistency (see Table 1.1). East Antarctic Ice Sheet is indicated in red, whilst the West Antarctic Ice Sheet is indicated in Green

1.1 Comparison with existing results

Basin volume estimates over the Antarctic ice sheet from altimetry commonly show a large spread. This is especially true when comparing radar and laser altimetry (RA and LA, respectively), but even for LA-only estimates, results can differ widely (see Table S7 and S8, Shepherd et al, 2012). Differences may stem from the calculation technique used to obtain the trends, the error thresholds, filtering methods, and correction terms applied (e.g, the inter campaign bias; Hofton et al. 2013; Shepherd et al. 2012; Gunter et al. 2014). In Shepherd et al. (2012), ICESat volume trend estimates from four groups over the same observation period range from 63 to 94 km³/yr for the East Antarctic Ice Sheet (EAIS), resulting in a spread of 31 km³/yr. For the West Antarctic

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Ice Sheet (WAIS), they range from -7 to -50 km³/yr, resulting in a spread of 43 km³/yr, far larger than the stated uncertainties. After correcting for firn compaction and density, the mass change estimates for the WAIS have an even larger spread (Table S6.4).

For our comparison (Table 1.1), we use the ICESat basin results from Ben Smith (BS, University of Washington) as used in Shepherd et al. (2012). The largest differences, due, primarily, to the sparse coverage and greater relief, occur on the Antarctic Peninsula. Other large differences are found over parts of EAIS. The basins which show the largest differences are mostly located near the margins of the EAIS, where track spacing is larger and more points are missing in the non-interpolated data set. Also, it can be seen that these are basins where the REGINA dataset favours Envisat data over ICESat data (Fig. 1.2, ATBD [AD-3]). In the following, some basins are grouped to achieve a consistent coverage between REGINA and Smith data sets (Table 1.1).

Combined basin A (basin 1 & 24 & 27 in Smith, basin 1 & 24 in REGINA) yields an absolute difference of -19.6 km³/yr (REGINA without interpolation). However, the uncertainty on the basin is almost as large as the signal itself, due to the large track spacing and poor coverage over the southern Peninsula. In the interpolated version the difference reduces to -9.2 km³/yr, or 26 % with respect to the whole basin. Basin 3 shows a similar relative difference of 29 %. Interpolation does not alter the results in this case, however. The uncertainty of the REGINA datasets is relatively small and comparable to the Smith uncertainty for the same basin.

Basin E again suffer from large relative differences in the un-interpolated version, but the interpolated version agrees very well with the Smith estimate. For combined basins H, as well as for basin J, the differences are slightly increased through the interpolation.

Basin K shows large relative and absolute differences in the comparison. REGINA without (with) interpolation sees a larger loss at -13.4 (-11.1) km³/yr than Smith whose estimates yield a moderate increase at +3.3 km³/yr. Here, Envisat may be picking up more data points around the coast where we observe large rates of ice loss at Totten glacier (e.g. Rignot et al. 2008).

For basin L, the interpolation removes a large part of the differences.

For West Antarctica, the agreement is very reasonable over basins P to U. For basin U, the relative difference is increased by the interpolation. Here, the uninterpolated version is much closer to the Smith estimate.

In basin 19, the relative difference is large due to the low signal-to-noise ratio in the REGINA datasets.

The largest absolute differences are found on the Peninsula (basin V). This is an expected result – with the extremely poor coverage and high uncertainties, few points will have a large impact in the interpolation, leading to large differences. It is important to note that both the method for determining the elevation differences and how they are interpolated/extrapolated spatially can have an impact on the volume change estimates (Sørenson et al, 2011; Hurkmans et al, 2014).

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Table 1.1 Volume estimates and uncertainties (unc.) from radar altimetry (RA), laser altimetry (LA) for 2003 to 2008, adapted from Shepherd et al. (2012), and comparison with volume estimates from REGINA and Ben Smith (Smith, University of Washington). Red marks differences (i.e. REGINA ─ Smith) greater or equal than 9 km³/yr in magnitude. The conversion factor used was the density of ice (917 kg/m³).

Basin Volume rate (km³ / yr) Difference (km³ / yr)

REGINA Smith Comb. REGINA (no int.) REGINA (with int.) Smith REGINA

(no int) ─ Smith

REGINA (with int) ─ Smith

dV/dt unc.dV/dt dV/dt Unc.dV/dt dV/dt unc.dV/dt dV/dt

1&24 1&24&27 A 25.4 21.5 35.9 24.4 45.0 1.8 -19.6 -9.2

2 2 B 5.4 2.2 4.4 2.2 6.1 1.1 -0.7 -1.7

3 3 C 29.5 4.2 29.5 4.1 38.2 4.7 -8.7 -8.7

4 4 D 12.2 7.6 11.3 8.2 14.5 1.0 -2.3 -3.2

5&6 5&6 E 4.4 19.4 -5.9 20.5 -5.7 3.0 10.0 -0.2

7 7 F 6.7 9.4 5.1 10.0 1.4 2.8 5.3 3.7

8 8 G 4.1 12.7 5.0 14.6 3.3 2.4 0.8 1.7

9&10 9&10&11 H 21.7 14.8 14.8 15.8 23.6 3.0 -1.8 -8.7

11 12 I -7.9 10.6 -7.5 12.1 0.0 0.5 -7.9 -7.5

12 13 J -18.4 13.7 -21.8 15.3 -12.8 2.8 -5.7 -9.1

13 14 K -13.4 10.0 -11.1 11.3 3.3 0.8 -16.7 -14.4

14 15 L 5.7 14.1 -12.6 16.3 -10.3 2.2 16.0 -2.4

15 16 M -6.8 7.7 -2.7 8.2 -1.5 4.5 -5.3 -1.2

16&17 17 O -6.3 10.8 -5.5 11.0 -0.5 4.9 -5.8 -5.0

18 18 P 17.3 2.4 17.1 2.4 24.5 0.5 -7.3 -7.5

19 19 Q -0.6 2.3 -0.6 2.4 2.5 0.8 -3.1 -3.1

20 20 R -19.7 10.3 -20.0 10.9 -14.5 4.9 -5.2 -5.5

21 21 S -62.3 8.8 -65.2 10.0 -70.0 0.8 7.7 4.8

22 22 T -35.2 5.9 -33.3 6.2 -28.9 1.2 -6.3 -4.4

23 23 U 4.9 9.0 9.0 10.2 4.7 1.4 0.2 4.3

25 25&26 V -21.8 12.4 -33.5 17.8 -86.7 0.4 64.9 53.2

AntIS AntIS -55.1 51.2 -87.8 57.6 -63.8 12.1 8.7 -24.0

1.2 Quantification of improvement

Overall, the combined REGINA data set appears to correct the overestimation of volume trends over the EAIS which occurs when using only LA (Table 1.2). The comparisons with results from Shepherd et al. (2012) shows that for WAIS, ICESat estimates are in most cases significantly higher than the corresponding RA observations. In contrast, for East Antarctica the use of RA trends reduces the positive rates, which is more consistent with other remote sensing methods (Table 1.2); For the EAIS, the REGINA estimate is reduced to 36.8 km³/yr (2.9 km³/yr with interpolation) when the IMBIE RA estimate lies at 24 km³/yr. For the WAIS, the REGINA estimate shows higher

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loss rates at -93.8 (-91.3 interpolated) km³/yr. Slight differences in the observation period must be taken into account in these comparisons.

Table 1.2 Volume estimates from radar altimetry (RA), laser altimetry (LA) for 2003 to 2008, adapted from Shepherd et al. (2012), and comparison with volume estimates from REGINA and Smith et al. 2014, summarized into EAIS and WAIS (see Table 1.1 and Figure 1.1).

Region Volume rate (km³ / yr)

(Comb. Basins) RA LA REGINA (no int.) REGINA (with int.) Smith

EAIS* (B to O) 24.0 118.9 36.8 2.9 59.5

WAIS* (P to U) -58.9 -65.4 -95.6 -93.1 -81.7

* without basins A and V

1.3 Analysis of the errors / uncertainties

We use a threshold of +-5 m/yr on the trend and a threshold of 8 m/yr on the 1-sigma standard error for our dataset. For Envisat, we observe very large, unphysical errors (> 1000 m/yr) for a very small number (< 2.5 %) of points which are located at the margins of the ice sheet. These errors most likely stem from problems with loss of lock in these steep regions. This contributes to a considerably larger uncertainty in the combined data set.

The influence of RA data with its larger errors is visible in the basin errors when comparing them to ICESat-only estimates. Errors range from 2.2 to 21.5 km³/yr for the non-interpolated REGINA data set, and 24.4 km³/yr for the interpolated data set. For ICESat only, the errors range from 0.4 to 4.9 km³/yr. The overall error for all basins is 51.2 km³/yr for REGINA (57.6 km³/yr interpolated), compared to 12.1 km³/yr for the Smith data set. On average, uncertainties in the REGINA data set are estimated to be a factor five greater than in the Smith data set. It is likely that this is an overestimation, since the majority of data points are constituted from ICESat. This needs further investigation.

2 GPS solutions in Antarctica

Here we compare the vertical trends at individual sites derived from GPS data during the REGINA project (data span 1995.0-2013.7) with those available from two previous studies, Thomas et al.

2011 (data span 1995.0-2011.0) and Argus et al. 2014 (data span 1994-2012). The selected series used from the ensemble of processing solutions created is g1-1-p (as described in the Section 2, ATBD [AD-3]). The project found differences within the ensemble averaging 0.1 mm/yr in magnitude (and up to ~0.2 mm/yr) due to processing effects. While the Global Geodetic Observing System aims include geodetic measurements accurate to this level (Plag and Pearlman, 2009), it is apparent that for most sites, analysis choices such as antenna offset/metadata selection, and the method of dealing with the non-white noise, for which GPS is known when performing trend estimation, have a much larger potential effect, at least for Antarctic sites.

A total number of 85 Antarctic sites were processed and provided to the REGINA project. A larger number would have been available but the permissions needed to use the data for those stations

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were regrettably not forthcoming during the time available. Due to the considerable number of stations, they are divided into groups using the methods used to derive the linear secular trends and uncertainties; details on the estimation of trend and the associated uncertainty are found in Section 2.4 of the ATBD [AD-3]. All values shown are in mm/yr, with uncertainties reflecting 1-sigma standard errors. Sites with particularly complex non-linear timeseries such as those at O’Higgins and Palmer in the Antarctic Peninsula are omitted here, as comparison with different studies is potentially misleading due to the effects of different measurement time periods. In total four stations (of the 85) were not considered in this comparison (haa1, ohi2, ohig and palm); however, time series plots and linear secular trends are provided for all stations in Appendix A2 and Appendix A3.


Long running sites (those with over 2000 daily epochs of data available) were analysed using the CATS software (Williams, 2008) and the trends and errors produced by CATS for these sites (listed

in Appendix A2) can be seen in Table 2.1 and

Figure 2.1. In general the rates for these sites match well, though even here not all match to within the 1-sigma standard error bars.

Table 2.1 Trends (mm/yr) and uncertainties (mm/yr, 1-sigma standard errors) from CATS software compared with trends and uncertainties from two other published studies

REGINA Thomas et al. 2011 Argus et al. 2014

Trend Unc. Trend Unc. Trend Unc.

aboa 0.6 0.5 1.4 0.8

cas1 1.5 0.2 1.2 0.4 1.7 0.8

crar 0.7 0.4 1.0 0.7 1.0 0.6

dum1 -0.3 0.3 -0.8 0.5 –0.2 0.8

maw1 -0.4 0.2 0.1 0.4 0.2 0.6

mcm4 0.8 0.2 0.7 0.4

sctb 0.9 0.5 0.6 1.1

syog 1.1 0.2 2.3 0.4 0.6 0.8

tnb1 0.1 0.5 -0.2 0.8 –0.4 1.0

vesl 0.4 0.3 1.1 0.5 1.5 0.8

Mean 0.5 0.3 0.7 0.6 1.0 0.8

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Figure 2.1 Trends and uncertainties (mm/yr, 1-sigma standard errors) for REGINA sites assessed using CATS compared with two other studies

Table 2.2 and

Figure 2.1Figure 2.2 show sites with fewer than 2000 daily epochs of data. Data available from these sites either has a shorter time span, or gaps in the available data. For these sites, the trends and uncertainties were derived using propagation of the median noise values derived from the CATS sites, as described in the section 2.4 of the ATBD [AD-3]. Essentially, a covariance matrix for the observations is constructed using the median white noise scaling factor and power law noise values, then least squares is used to solve for the parameters and their covariance matrix, with the standard errors taken from the diagonal. These values show a somewhat larger spread between the different studies, where comparison is possible, though almost all sites fall within the 1-sigma limits.

Table 2.2 Propagated trends and uncertainties (mm/yr, 1-sigma standard errors) from REGINA compared with those from two other studies



belg -1.4 0.7 3.0 1.5 0.8 2.4

dupt 11.5 1.1 12.4 2.5

fonp 13.5 1.8 14.8 3.4

frei -4.4 0.7 –2.9 1.4

hugo 0.9 1.3 1.7 3.6

robi 8.7 1.5 8.7 3.2

rotb 5.0 0.4

roth 5.5 1.4 5.4 1.4

smrt 1.2 0.9

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

aboa cas1 crar dum1 maw1 mcm4 sctb syog tnb1 vesl

Tre

nd

(m

m/y

r)


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svea 1.3 1.1 2.1 2.0 1.7 2.9

vnad 4.4 1.1 5.2 2.5

Mean 4.2 1.1 2.5 1.7 6.3 2.6

Figure 2.2 Propagated trends and uncertainties (mm/yr, 1-sigma standard errors) compared with those from two other studies

Table 2.3 provides data for those sites where further manual investigation of the series suggested that while the propagated trend appeared to be a reasonable representation of the data, other trends calculated based on subsets of the data suggested that the uncertainty was potentially larger. A new uncertainty was devised based on the spread in the calculated trends.

Table 2.3 Propagated trends (mm/yr), and uncertainties from spread of manual trends (mm/yr, 1-sigma standard error) compared with those from two other studies. *error for dal1 is excluded from mean as it is unrepresentative of overall solution.



a351 -0.9 1.8 0.8 1.3 1.1 3.5

a368 -0.2 1.2 0.4 1.0

arct -0.1 4.4

coat -0.1 7.3

dal1 4.9 * 34.4

for1 -0.2 2.9 -1.4 0.8

for2 -0.3 2.7 2.1 0.9

ftp1 -2.2 3.4 2.1 2.8

grw1 -7.0 8.6

mbl1 2.5 3.0 0.6 1.5

-10

-5

0

5

10

15

20

belg dupt fonp frei hugo robi rotb roth smrt svea vnad

Tre

nd

(m

m/y

r)


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rob1 5.4 5.1 7.5 2.6

wasa 0.6 3.2

Mean 0.2 4.0 1.7 1.5 1.1 3.5

For sites with only two campaigns it was considered that there is no protection against systematic metadata uncertainties occurring, so an uncertainty of +- 100mm/yr was assigned. If there was other supporting evidence, such as two sites close together or a subsequent site with similar rates, the uncertainty was reduced to ±10 mm/yr. The numbers for these sites can be found in Table 2.4. Thomas et al. 2011 and Argus et al. 2014 chose not to include many of these sites, but we feel it is sufficient to assign them an appropriately large uncertainty to identify the lack of robustness of the results.

Table 2.4 Propagated trends (mm/yr), and uncertainties of 100 mm/yr if only two campaigns, or 10 mm/yr if two campaigns but extra supporting evidence available. All units mm/yr. Sites where names differ slightly are shown to highlight possible differences.



art1 -3.1 10

cjam -2.3 100

cwal 0.4 100

dall -17.0 100

elph 6.3 100

esp1 5.6 100

mar1 7.1 10

mbl2 2.3 10 0.2 4.1

mirn 24.4 100

mtcx -3.8 10

ohg1 4.5 10

pal1 8.1 10

pra1 4.2 10

prat -9.6 100

prtt -5.0 100

reyj 151.3 300

rot1 6.5 10

sig1 23.0 100

smr1 0.5 10

sppt 12.9 100

ver1 0.3 100

ver3 -6.2 100

w01a -0.3 10 -2.5 1.7 w01a–howe 0.9 1.2

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w01b 1.4 10 -3.1 1.7

w02a 0.3 10 w02a/pece 2.8 1.2 w02a–pece –1.2 1.9

w02b 2.3 10 0.5 1.9

w03a -1.4 10 -3.2 1.8 –1.1 2.4

w03b 1.7 10 -1.7 1.8

w04a 3.7 100 3.0 1.1

w05a 2.3 10 3.5 2.0

w05b 7.4 10 5.3 1.2

w06a -2.2 100 -2.2 2.4 –4.7 4.4

w07a 3.3 100 3.3 2.1 4.6 3.1

w08a -1.5 100 w08a/b/sugg 1.3 1.3

w09a 2.2 100 w09 4.5 2.6

Finally, where manual assessment using subsampling of the series suggested that the propagated trends were not a good representation of the series, a value more representative of the variety of trends was selected and the uncertainty again adjusted to the spread of the trends. Table 2.5 shows the results for these sites.

Table 2.5 Manual trends (mm/yr) and uncertainties from spread of manual trends (mm/yr, 1-sigma) compared to two other studies



bhil 2.9 4.4

bren 3.1 1.1 3.9 1.6 2.1 3.7

capf 4.0 1.4 15.0 4.2

dav1 -1.6 0.6 -0.9 0.5 –0.8 1.0

eacf -4.8 15.0

ferr -5.5 31.0

flm2 3.8 11.7

fos1 2.9 9.4 2.1 0.4 2.9 1.2

gmez -0.6 10.0

jnsn -3.6 10.0

mait 0.4 1.1 0.1 0.6 1.3 0.7

mbl3 1.3 17.9 0.1 2.0

trve -4.6 10.0

Mean -0.2 9.5 1.1 1.0 5.3 2.1

2.2 Quantification of improvement

In conclusion, we have processed a comprehensive Antarctic GPS dataset (where available) which for continuous sites spans a longer period than the previous studies of Thomas et al. (2011)

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(1995.0-2011.0) and Argus et al. (2014) (1994-2012) and is therefore expected to be more robust. This is borne out in the realistic (coloured noise) confidence limits that we have derived for the vertical rates of these sites (Table 2.1 and Table 2.2), which are generally lower than for the previous studies. For sites where there is potential doubt over the quality of the metadata or the behaviour of the site (Table 2.3, Table 2.4, and Table 2.5) we have adopted a ‘conservative but realistic’ approach to assigning a confidence limit, which allows the vertical rates for a greater number of sites to be used in subsequent parts of REGINA.

3 Gravity field observations


Velicogna and Wahr (2013) discuss the use of standard GRACE monthly solutions of the Release-5 era for ice sheet applications. They use the CSR RL05 solutions solved up to degree 60 and apply a 300 km Gaussian filter for the case of Antarctica. We adopt their approach as a benchmark, extending their observation period to the REGINA period (2003 – 2009).

Fig. 3.1 compares a linear trend derived from a filtering according to Velicogna and Wahr (2013) with the linear trend derived for REGINA. The comparison shows that the REGINA field preserves more signals of ice mass loss, such as in the Amundsen Sea Sector, at the Antarctic Peninsula or at Totton Glacier in East Antarctica, and also of mass gain such as with the slowing of the Kamb Ice Stream draining into Ross Ice Shelf or with snow accumulation in Dronning Maud Land. At the same time, the noise level, as obvious from the stripy small-scale features, is similar in both fields, even though the inclusion of signal beyond degree 60 by the REGINA field entails additional small-scale noise.

Figure 3.1: Rate of equivalent water-height change (mm/yr) from a) CSR RL05 solutions up to degree 60, with a 300 km Gaussian filtering, b) the REGINA study. c) Difference of the trends plotted in a) and b). Note the different color scale.

3.2 Analysis of the errors / uncertainties

It is particularly important to assess the impact that the Swenson filtering has on actual geophysical signals. For this aim, Fig. 3.2 compares the REGINA field with a field obtained in the same way but without the Swenson filtering step. The comparison shows that the Swenson filter indeed removes the meridional error patterns very efficiently, without an obvious corruption of actual signal. A single exception may be observed for the Antarctic Peninsula, where the negative signal is somewhat reduced by the filtering, owing to the North-South elongated nature of this signal.

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Figure 3.2: Rate of equivalent water-height change (mm/yr) a) from a treatment like the REGINA field, but with omission of the Swenson filter, b) of the REGINA field. c) Difference between the trends shown in a) and b). Note the different color scale

As another test we apply the Swenson filter to the final REGINA GIA model, as shown in Fig. 3.3. The GIA field is virtually unchanged by the filtering, regarding the color scale in (c) being 50 times smaller than in (a) and (b).

Figure 3.3: Rate of geoid-height change (mm/yr) for a) the GIA estimate derived within REGINA (RE000-030, Sim.#29, Table A1.1), b) the same model but after Swenson filtering. c) Difference between the two fields. Note the different color scale.

As a final uncertainty assessment, we compare the GRACE trend obtained from the GFZ solutions with a trend obtained analogously from the experimental CSR solutions expanded up to degree 96. For the comparison with use the solutions only up to degree 90, which is the maximum degree of the GFZ solutions. Fig. 3.4 shows the comparison. The difference between the fields based on GFZ and CSR solutions show no obvious correlation to actual geophysical signals.

The difference between the two fields reveals differences in the noise contained in the two series. From the formal errors of the trends, the noise level of the CSR field is somewhat lower than that of the GFZ field (except for the Filchner Ronne Ice Shelf). This seams at odds with the assessment of uncertainties in the ATBD (Section 3.5, [AD-3]). However, there we have not truncated the CSR fields at degree 90 prior to the analysis, so that the Swenson filter had to deal with noise extended up to degree 96 for CSR, but only up to degree 90 for GFZ..

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Figure 3.4: a) map of "REGINA Trend", b) Map of trends derived in the identical way, but from the CSR 96 solutions, truncated at degree 90, c) Difference between the fields in a) and b), and d) and e) associated formal errors of the trends, based on the residuals of the linear+quadratic + annual + semi-annual fit in the spatial domain.

4 GIA prediction

The sensitivity of the GIA estimate to the choice of the Earth structure, the effect of the ductile layer and the comparison with published models was undertaken in the VR [AD-4].

For the analysis of the impact on CryoSat-2 and GRACE data, the two end members of GIA simulations are employed, i.e. hL: 30 km with DL (Sim.#29) and b) hL: 200 km without DL (Sim.#59, (Appendix A1).

5 Combined GIA estimate for Antarctica

The following section presents the analysis of the impact of the GIA-induced elevation and gravity rates in Antarctica on volume and mass balances from CryoSat-2 and GRACE, respectively.. As seen from Fig. 5.1 most elevation change from altimetry lie within ca. ± 0.5 m/yr for longer periods (2003 to 2009; ICESat/Envisat), as well as for shorter periods (2011 to 2012; CryoSat-2) ; exceptions are along the Antarctic Peninsula, the Amundsen Sea Embayment and Dronning Maud Land, where elevation rates can reach up to 5 m/yr. Compared to this magnitude, the inferred magnitude of the elevation rate produced by GIA is small with an upper bound of < 30 mm/yr (see Fig. 2.6, VR [AD-4]).

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Figure 5.1 Rate of elevation change (m/yr) of derived from a) Envisat/ICESat for the years 2003 to 2009 (Baseline Requirement [AL01] & [AL02]) used to derive the GIA estimate, and b) CryoSat-2 for the years 2011 to 2012 (courtesy of V. Helm, AWI Bremerhaven) used for the impact analysis.

However, summed up over the entire continent of 12 x 10 6 km² a GIA induced offset of on the mm-level can induce cause a significant bias in the estimated rates of volume change. It is the purpose of this section to assess this impact in detail.

It should be stated that this impact analysis focusses on the GIA-induced displacement as a response to past load change. Instantaneous elastic deformation to present ice-mass changes as shown in Fig. 5.2 is excluded, even though the elastic signal may constitute a significant portion of the deformation signal in regions of ongoing mass changes.

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Figure 5.2 Rate of radial displacement (mm/yr) caused by the a) elastic response to present-day ice-mass changes and b) GIA as separated in REGINA. The underlying elastic Earth structure is PREM, while the viscoelastic Earth structure is ℎ𝐿: =60 km without ductile layer Sim.#61 and (Sim.#13, respectively, Appendix A1). Please note that this graph displays the GPS sites relocated on the geodesic grid used for the combination, and some sites overlap.

5.1 Impact on CryoSat-2 volume rates

We assess the impact of the GIA estimate on elevation rates from CryoSat-2 for the 25 Antarctic drainage basins shown in Fig. 1.1. Even though the GIA fields are available on a 10 km x 10 km Polar Stereographic grid, we choose to integrate over these entities to allow for an easier comparison. The calculation of the of the volume rate 𝑣𝑗 for a basins is straight forward according

to 𝑣𝑗 = ∑ 𝑢𝑖 𝐴𝑖𝑖 𝛼𝑖 , where i is the index of grid points within drainage basin j and 𝐴𝑖 = 𝑐𝑜𝑛𝑠𝑡. =

100 km² is the nominal area represented by each grid point, while 𝛼𝑖 accounts for the distortion o the true area with respect to the nominal area (𝛼𝑖 = 1 at true latitude of 71°S; range of ca. 0.87 to 1.06 within data domain). The rate of radial displacement is 𝑢𝑖. In the following, basins 2 through 17 are considered East Antarctica, 1 & 18 through 23 West Antarctica and basin 24 & 25 the Antarctic Peninsula.

Fig. 5.3 shows the volume rates associated with GIA for the end members of the Earth structure ensemble, as well as those obtained with W12a and AGE1. In general, volume rates lie within ± 4 km³ / yr. Integrated over the basins, the end members of the REGINA ensembles exhibit similar volume rates when, independent whether a posteriori smoothing is applied or not (see VR [AD-4]).

There is a pronounced positive volume change (basins 11 & 12, East Antarctica) associated with the strong uplift anomaly in the western part of Wilkes Land (for a comparison of spatial patterns see VR, Fig. 2.6 and Fig. 2.7 [AD-4]). However, since none of the published GIA estimates contain this anomaly, it is most likely a signature of poorly removed surface-mass changes (see VR [AD-4]). A further discussion on identifying this anomaly as elastic or viscoelastic is given in the following section.

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Figure 5.3 Rate of volume change (km³/yr) produced by the end members of the ensemble of GIA estimates of REGINA, a) ℎ𝐿: 30 km with DL (Sim.#29); Appendix A1), and b) ℎ𝐿: 200 km without DL (Sim.#59, Appendix A1), as well as of the correctiofns b) W12a (Whitehouse et al. 2012) and d) AGE1 (Sasgen et al. 2013).The light colored bars in a) and b) indicate the results obtained a posteriori Gauss filtered GIA fields (please see [AD-4]).

A strong negative volume rate of ca. -3 km³/yr is estimated for basin 3, Coats Land, East Antarctica, which is consistent with W12a, as discussed in [AD-4]. However, W12a and AGE1 produce considerably stronger uplift for the continental areas south of the Filchner-Ronne ice shelf (basins 1 and 2) than the REGINA estimates, which are close to zero for these basins (slightly positive for hL: 200 km).

When summed up over the entire continent, volume rates for the REGINA models lie between 3.8 and 6.8 km³/yr, similar to W12a (5.0 km³/yr) and AGE1 (10.0 km ³/yr). Compared to a volume rate on the order of 200 km³/yr (CryoSat-2; 2011 to 2012, this correction appears to be of secondary importance.

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5.2 Effect of GPS coverage

In the combined estimate, surface-displacement rates from GPS are used to improve the correction for surface-mass trends derived from the altimetry data (see ATBD, [AD-3]). It is obvious that this approach has a local impact only, limited by the spatial coverage of GPS sites with good-quality. Each site is unique in its location (see Section 2), temporal coverage and record quality, and, in principle, the effect of including a measurement in the combination needs to be individually assessed. To assess the effect of the GPS data coverage, we apply three sub-sets of the GPS data (Table 5.1; Appendix 3)-

Table 5.1: Specifications of subsets of the GPS data underlying the GIA estimate in Fig. 5.4.

GPS data subset Source Initial # Selected # Comment

GPS A REGINA 85 49 Stations with signal-to-noise ratio > 0.25; excluded FTP1 (offset likely in 2002) and MTCX (no coverage in GRAC period)

GPS B REGINA & Groh et al. 2012

88 50 Same as GPS A, but including PIG2

GPS C REGINA & Groh et al. 2012

88 88 All sites available

Fig. 5.4 shows the GIA estimate from REGINA (hL: =60 km, without DL) without including GPS rates, as well as when including different subsets of the data. Without GPS rates, a pronounced negative anomaly is visible along the southern Antarctic Peninsula, resulting from an overestimation of the mass gain in this area; the initial of density of 910 kg / m³ attributed to the altimetry measurement is too high for this region, which is prone to experience strong snow fall events. Including the GPS rates (e.g. GPS data set A, Fig. 5.4) identifies this overestimation and reduces the anomaly. A similar effect is visible in Coats Land and Dronning-Maud Land, East Antarctica. In addition, the positive anomaly along the Transantarctic Mountains, Ross Ice Shelf, is reduced, however, also changed in sign. Including the station PIG2 in the Amundsen Sea Embayment (Groh et al. 2012) retains the uplift anomaly observed there, which was attributed by the authors to a GIA.

An example, where the GPS rates presumably degrade the GIA estimate is shown for GPS data set C (all data, including Groh et al. 2012). As mentioned earlier, the GIA estimates show a pronounced uplift anomaly in the western part of Wilkes Land. In close proximity to this anomaly lies the GPS station MIRN. However, its record consists only of campaign data for the years 2006 and 2007 (Fig 5.5 and Appendix A.2) making it impossible to distinguish been an offset between the two measurements and a real “trend” in this one year period. In fact, including this station with uplift rates on the cm-level, increases the anomaly and leads to a greater disagreement with the published models. Another example are the stations MTCX and FTP1 (Appendix A.2), located close to the Ross Shelf, which show negative trends with good accuracy, by this contradicting other sites in this area.

Overall, the GPS rates are useful to separate the GIA signatures. A greater spatial and temporal coverage is desirable, particular in key regions of elastic and viscoelastic deformation (see Scientific Roadmap, [RD5])

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Figure 5.4 Rate of radial displacement (mm/yr) of the GIA estimate from REGINA, based ℎ𝐿: 60 km, without DL (Sim.#13, Appendix A1) and GPS rates a) not included b) most reliable selected (GPS A, c) most reliable including those from Groh et al. 2012 selected (Amundsen Sea Embayment) (GPS B) and d) all GPS rates available (GPS C; REGINA & Groh et al. 2012).

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Figure 5.5 Time series of bedrock displacement at GPS station MIRN, along with temporal linear

trend (solid line) and its 1-sigma uncertainties (dashed lines). It is visible that the data consists only of two measurement campaigns in 2006 and 2007; therefore, an uncertainty of ± 100 mm/yr is attributed to the trend. However, since this is the only station close to the spurious the impact of this station remains is large despite its uncertainty (see Fig. 5.4, GPS B and GPS C).

5.3 Sensitivity to filtering of gravity fields

REGINA relies on temporal linear trends of the GRACE gravity fields, which are optimized in spatial resolution, while minimizing noise and signal corruption. The approach taken is de-striping according to Swenson & Wahr 2006 and subsequent Gaussian filtering (details in the ATBD [AD-3]). To assess the robustness of the results to different GRACE filtering, an alternative post-processing chain is applied; it consists of de-striping by statistical filtering and subsequent Wiener filtering (Table 5.2; more details in Section 2.2.2 of [AD-1]).

Table 5.2: Specifications of GRACE post-processing steps for the REGINA reference (SW+G200) and an alternative (STAT+W) filtering.

GRACE filtering Release De-striping Smoothing Comment

SW+G200 CSR RL05 (degree and order 90)

Swenson & Wahr 2006

Gaussian 200 km

REGINA reference post-processing

STAT+W CSR RL05 (degree and order 96)

Omission of statistical insignificant coefficients

Wiener optimal filtering

Alternative post-processing

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Fig. 5.6 shows the GIA estimates for the same Earth structures and GPS data sets as presented in Fig. 5.5, but for this alternative filtering method. The main features in 5.5 and 5.6 are very similar in terms of their location, even though some differences are noticeable along the Antarctic Peninsula and close to the Ross Ice Shelf. Overall the magnitude resulting from the alternative filtering appears to be a bit smaller and anomalies are less pronounced, being most likely a result of effectively stronger smoothing by the Wiener filtering.

However, the differences between both (very different) filtering approaches are small, as is the difference between using the GRACE releases CSR RL05 and GFZ RL05 (see Fig. 3.4). We therefore consider GRACE not to be the dominant source of uncertainty in this approach to GIA determination.

Figure 5.6 Same as Fig.5.5, but with statistical & Wiener filtering (STAT+W) applied.

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5.4 Impact on GRACE mass balances

Next, we will present results on the geoid-height change associated with the GIA estimates from REGINA and determine the impact on ice-mass balances derived from GRACE. Fig. 5.7 shows the spatial geoid rate over Antarctica, separated into its surface-mass and GIA component (analogue to Fig. 5.2 for the rate of radial displacement); for completeness, we present the data for both types of GRACE post-processing. It is visible that the surface-mass signal dominates the geoid rates, particularly over the Amundsen Sea Embayment. The separated GIA signal is lower in magnitude. It also does not share many spatial characteristics with the surface-mass signal, which is an indication that the GIA signature is not merely a “ghost” produced by the removal of the surface-mass signal.

Fig. 5.8 presents the apparent mass change for the 25 Antarctic drainage basins. It is similar in its signature to the same diagram for the rate of volume change (Fig. 5.3), however, impact on the GRACE mass rates is about three time larger than on the CryoSat-2 volume rates. For all basins, the GIA-induced rate of mass change lies within ± 12 Gt/yr. The REGINA fields and W12a both show negative GIA corrections for basin 3, opposed to AGE1 which is positive for all basins.

The sum over all basins lies within the range 26.0 to 38.1 Gt/yr for the GIA estimates from REGINA, which is somewhat smaller than the previous values of 55.8 and 51.8 associated with W12a and AGE1; this

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Figure. 5.7 Rate of geoid-height change (mm/yr) for the separated mass change components a) and b) ice mass change, as well c) and d) GIA. The geoid rates a) and c) are obtained using the reference filtering of REGINA (SW+G200; optimized Swenson & Wahr, 2006 & Gaussian 200 km), while b) and d) are obtained using an alternative approach based on statistical filtering and Wiener filtering (STAT+W; detailed in [AD-1]). The Earth structure underlying the estimate is Sim.#13 (Appendix A1).

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Figure 5.8 Rate of mass change (Gt/yr) produced by the end members of the ensemble of GIA estimates of REGINA, a) ℎ𝐿: 30 km, DL, and b) ℎ𝐿: 200 km, without DL, as well as of the corrections b) W12a (Whitehouse et al. 2012) and d) AGE1 (Sasgen et al. 2013).The light colored bars in a) and b) indicate the results from the a posteriori Gauss filtered GIA fields (please see [AD-4], Section 2.4)

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5.5 Remark on uncertainties

Fig. 5.9 shows uncertainties for the rate of radial displacement and rate of geoid-height change obtained by error propagation of the ICESat/Envisat and GRACE uncertainties. In the current version of the altimetry data, uncertainty is larger than the signal (see [AD-4]) and dominates over the GRACE uncertainties in the combined estimate. If the current estimate of uncertainty is accurate, the to-be inferred GIA signal mostly lies within the uncertainty range for most regions, preventing any confidence in the recovered estimate. However, it is possible that the altimetry uncertainties are overestimated; comparison with published uncertainties (see Section 1) may suggest an overestimation up to a factor of five.

Figure. 5.9 Rate of radial displacement (mm/yr) of GIA estimate of REGINA, a) ℎ𝐿: 60 km, without DL (Sim. 13, Appendix A1) and b) uncertainty of GIA estimate as obtained by propagation of GRACE & altimetry uncertainties; both without GPS constraint. Please note different scales in a) and b).

In addition to the uncertainties of the input data sets we need to account for the spread by the choice of the Earth structure (e.g. Fig. 2.6 in [AD-4]). Table 5.3 summarizes estimates of CryoSat-2 volume rates along with the two end members of the GIA simulations (Sim. #29 and #59). In addition to the per-basin aggregated values shown Table 5.3, 10 km x 10 km Polar Stereographic grids are made available.

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Table 5.3 Rate of volume change (km³/yr) obtained from CryoSat-2 data for the years 2011 to 2012 (courtesy of V. Helm, AWI), as well as the elevation rate corrections for GIA based on the two end members of GIA simulations (Sim. 29 and 59) identified in Appendix A1.

Basin Volume rate (km³ / yr)

REGINA CryoSat-2 R000-030 DL R000-200 no DL

dV/dt err dV/dt dV/dt dV/dt filt. dV/dt dV/dt filt.

1 -29.6 0.5 0.1 -0.2 0.2 0.3

2 -41.6 0.2 0.5 0.4 0.6 0.7

3 -55.1 0.1 -3.1 -2.7 -3.1 -2.8

4 3.4 0.2 -0.2 -0.2 -0.2 W-0.2

5 88.3 0.2 0.4 0.2 0.4 0.2

6 126.6 0.2 0.2 0.1 0.2 0.1

7 54.8 0.3 0.5 0.3 0.5 0.3

8 53.9 0.4 0.5 0.2 0.5 0.2

9 -17.1 0.1 -2.2 -1.9 -2.1 -1.8

10 1.8 0.2 -0.4 -0.2 -0.4 -0.2

11 62.0 0.2 3.5 2.9 3.5 2.9

12 -13.5 0.3 2.4 2.1 2.4 2.2

13 75.2 0.3 0.7 0.6 0.7 0.6

14 26.0 1.8 0.3 0.1 0.2 0.1

15 8.6 1.1 -0.4 -0.1 0.2 0.2

16 3.0 0.5 0.3 0.3 0.3 0.4

17 -16.9 0.9 0.8 0.9 0.9 0.9

18 24.3 0.2 1.4 0.9 0.7 0.7

19 10.0 0.1 0.0 0.1 0.0 0.2

20 12.7 0.2 -0.2 0.0 0.1 0.1

21 -72.5 0.2 0.3 0.1 0.3 0.2

22 -74.3 0.1 -0.5 -0.4 0.0 0.0

23 -11.2 0.2 -0.7 -0.3 0.0 0.0

24 -4.2 1.2 0.4 0.6 0.6 0.5

25 5.8 1.3 0.5 0.2 0.3 0.1

AntIS 220.7 3.1 5.3 3.8 6.8 13.3

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6 Summary

The REGINA team has improved the quality of linear trends derived from altimetry, GRACE and GPS data sets. In addition, viscoelastic Earth modelling was advanced to explore the effect of a ductile crustal layer on the deformation and gravity field. The improved data sets and modelling results were consistently combined in a new framework to derive an improved GIA estimate for Antarctica. Finally, the impact of the inferred GIA signal on CryoSat-2 and GRACE measurements was determined (Table 5.3). An outlook on how to proceed from the status achieved is presented in the Scientific Roadmap.

7 References

Argus D. F., Peltier W. R., Drummond R., Moore A. W. (2014) The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based on GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories Geophys. J. Int. 198 (1): 537-563 first published online May 29, 2014 doi:10.1093/gji/ggu140

Groh, A., Ewert, H., Scheinert, M., Fritsche, M., Rülke, A., Richter, A., Rosenau, R., Dietrich, R. (2012). An investigation of Glacial Isostatic Adjustment over the Amundsen Sea sector, West Antarctica. Global and Planetary Change, 98–99(0), 45-53. doi: http://dx.doi.org/10.1016/j.gloplacha.2012.08.001

Gunter, B. C., Didova, O., Riva, R. E. M., Ligtenberg, S. R. M., Lenaerts, J. T. M., King, M. A., van den Broeke, M. R. & Urban, T. (2013). Empirical estimation of present-day Antarctic glacial isostatic adjustment and ice mass change. Cryosphere Discussions, 7(4).

Hurkmans, R. T. W. L., Bamber, J. L., Davis, C. H., Joughin, I. R., Khvorostovsky, K. S., Smith, B. S., & Schoen, N. (2014). Time-evolving mass loss of the Greenland ice sheet from satellite altimetry. The Cryosphere Discussions, 8(1), 1057-1093.

Hofton, M. A., Luthcke, S. B., Blair, J. B. (2013). Estimation of ICESat intercampaign elevation biases from comparison of lidar data in East Antarctica.Geophysical Research Letters, 40(21), 5698-5703.

Plag H.-P., Pearlman M. R. (2009) Global geodetic observing system: meeting the requirements of a global society on a changing planet in 2020. Springer, Berlin

Rignot, E., Bamber, J. L., Van Den Broeke, M. R., Davis, C., Li, Y., Van De Berg, W. J., & Van Meijgaard, E. (2008). Recent Antarctic ice mass loss from radar interferometry and regional climate modelling. Nature Geoscience, 1(2), 106-110.

Sasgen, I., Konrad, H., Ivins, E. R., Van den Broeke, M. R., Bamber, J. L., Martinec, Z., and Klemann, V. (2013): Antarctic ice-mass balance 2003 to 2012: regional reanalysis of GRACE satellite gravimetry measurements with improved estimate of glacial-isostatic adjustment based on GPS uplift rates, The Cryosphere, 7, 1499-1512, doi:10.5194/tc-7-1499-2013.

Shepherd, A., et al. (2012), A reconciled estimate of ice-sheet mass balance, Science, 338, 1183–1189.

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Sørensen, L. S., Simonsen, S. B., Nielsen, K., Lucas-Picher, P., Spada, G., Adalgeirsdottir, G., ... & Hvidberg, C. S. (2011). Mass balance of the Greenland ice sheet (2003-2008) from ICESat data-the impact of interpolation, sampling and firn density. Cryosphere, 5(4), 173-186.Abshire, J. B., X. Sun, H. Riris, J. M. Sirota, J. F. McGarry, S. Palm, D. Yi, and P. Liiva (2005), Geoscience Laser Altimeter System (GLAS) on the ICESat Mission: On-orbit measurement performance, Geophys. Res. Lett., 32, L21S02, doi:10.1029/2005GL024028.

Swenson, S., & Wahr, J. (2006). Post-processing removal of correlated errors in GRACE data. Geophysical Research Letters, 33(8), L08402.

Thomas I. D., King M. A., Bentley M. J., Whitehouse P. L., Penna N. T., Williams S. D. P., Riva R. E. M., Lavallee D. A., Clarke P. J., King E. C., Hindmarsh R. C. A., Koivula H. (2011) Widespread low rates of Antarctic glacial isostatic adjustment revealed by GPS observations. Geophysical Research Letters, 38(22), L22302

Velicogna, I., and Wahr, J. (2013). Time‐variable gravity observations of ice sheet mass balance: Precision and limitations of the GRACE satellite data. Geophysical Research Letters, 40: 3055-3063.

Wessel, P., W. H. F. Smith, R. Scharroo, J. F. Luis, Wobbe, F. (2013) Generic Mapping Tools: Improved version released, EOS Trans. AGU, 94, 409-410.

Williams S. D. P. (2008) CATS: GPS coordinate time series analysis software. GPS Solutions 12(2):147–153. doi:10.1007/s10291-007-0086-4

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Appendix

A1. Earth model parameters

Table A1.1: Simulation number (Sim. #) and associated Earth model parameters; presence of ductile layer (DL; 1=no, 2=yes), depth of the mantle lithosphere (Depth; ML) and viscosity of the asthenosphere (Viscosity; AS). The equilibrium simulations used in the combination are marked with brown font.(modified after Table 4.2, ATBD, [AD-3]).

Sim. # DL Depth (km) Viscosity (1018

Pa s) Sim. # ctd. DL Depth (km) Viscosity (10

18 Pa s)

1 no 30 1 29† yes 30 1

2 no 30 3 30 yes 30 3

3 no 30 10 31 yes 30 10

4 no 30 30 32 yes 30 30

5 no 40 1 33 yes 40 1

6 no 40 3 34 yes 40 3

7 no 40 10 35 yes 40 10

8 no 40 30 36 yes 40 30

9 no 50 1 37 yes 50 1

10 no 50 3 38 yes 50 3

11 no 50 10 39 yes 50 10

12 no 50 30 40 yes 50 30

13 no 60 1 41 yes 60 1

14 no 60 3 42 yes 60 3

15 no 60 10 43 yes 60 10

16 no 60 30 44 yes 60 30

17 no 70 1 45 yes 70 1

18 no 70 3 46 yes 70 3

19 no 70 10 47 yes 70 10

20 no 70 30 48 yes 70 30

21 no 80 1 49 yes 80 1

22 no 80 3 50 yes 80 3

23 no 80 10 51 yes 80 10

24 no 80 30 52 yes 80 30

25 no 90 1 53 yes 90 1

26 no 90 3 54 yes 90 3

27 no 90 10 55 yes 90 10

28 no 90 30 56 yes 90 30

† RE000-030 (Reference end member soft Earth structure) 57 no 150 1

‡ RE000-200 (Reference end member strong Earth structure) 58 no 150 500

59‡* no 200 1

* East Antarctica

60* no 200 500

** Elastic

61** no n.a. ∞

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A2. GPS time series

Figure A2.1: a) Outline of 25 Antarctic drainage basins addressed in this study, as well as b) location observation period for the GPS sites used in REGINA. Please note that some stations overlap with others.

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Figure A2.2 Time series of surface displacement (mm) recorded at the 85 GPS sites, as determined within REGINA (three sites from Groh et al 2012 not shown). Value of linear trend and uncertainty (mm/yr) listed on top of diagram (trends included in GPS subset A printed in green; Section 5). The linear trend (solid orange line) and its 1-signa misfit range (dashed orange lines) are also shown. Note that offsets and linear trends are centred on first moment of distribution of measurement days. Vertical lines indicate start and end of common time period analysed in REGINA for the altimetry and gravimetry data sets (2003-2009). Circle colour and number inset represent the drainage basins shown in Fig. A2.1.

A3. List of GPS sites

Table A3.1: List of 85 GPS sites used in REGINA, as well as three ancillary sites taken from Groh et al. 2012 (alphabetical order). Shown are station id, rate, 1-sigma uncertainty and the method of trend and uncertainty estimation; cats: estimated by the CATS noise analysis software, prop: median uncertainty from CATS sites propagated, rman: manual intervention in rate due to potential systematic uncertainties, eman: manual intervention in uncertainty due to potential systematic errors. Also indicated are approximate location, attribution to the drainage basins shown in A2.1, as well as an identifier which sites are included (1) or not included (0) in the in the subsets of GPS data named GPS A, GPS B and GPS C used in the impact analysis (Section 5).

Station Rate (mm/yr) Unc. (mm/yr) Unc. Det. Lat (°) Lon (°) Basin GPS A GPS B GPS C

a351 -0.94 1.78 eman -72.9 74.9 10 1 1 1

a368 -0.2 1.2 eman -74.3 66.8 9 0 0 1

aboa 0.59 0.51 cats -73.0 -13.4 4 1 1 1

arct -0.11 4.4 eman -80.0 -80.6 1 0 0 1

art1 -3.13 10 eman -62.2 -58.9 25 1 1 1

bear* 28.1 10 No inf. -74.6 -111.9 21 0 0 1

belg -1.43 0.71 prop -77.9 -34.6 3 1 1 1

bhil 2.93 4.4 eman -66.3 100.6 12 1 1 1

bren 3.06 1.1 eman -72.7 -63.0 24 1 1 1

capf 4.04 1.4 eman -66.0 -60.6 25 1 1 1

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cas1 1.45 0.22 cats -66.3 110.5 12 1 1 1

cjam -2.32 100 eman -63.1 -62.7 25 0 0 1

coat -0.13 7.34 eman -77.8 162.0 15 0 0 1

crar 0.69 0.4 cats -77.8 166.7 15 1 1 1

cwal 0.42 100 eman -63.2 -62.2 25 0 0 1

dal1 4.88 34.4 eman -62.2 -58.7 25 0 0 1

dall -16.99 100 eman -62.2 -58.7 25 0 0 1

dav1 -1.62 0.64 eman -68.6 78.0 11 1 1 1

dum1 -0.3 0.32 cats -66.7 140.0 13 1 1 1

dupt 11.46 1.11 prop -64.8 -62.8 25 1 1 1

eacf -4.83 15 eman -62.1 -58.4 25 1 1 1

elph 6.32 100 eman -61.2 -55.1 Island 0 0 1

esp1 5.61 100 eman -63.4 -57.0 25 0 0 1

ferr -5.47 31 eman -62.1 -58.4 25 0 0 1

flm2 3.82 11.7 eman -77.5 160.3 15 1 1 1

fonp 13.53 1.75 prop -65.2 -61.6 25 1 1 1

for1 -0.23 2.9 eman -70.8 11.8 5 1 1 1

for2 -0.31 2.7 eman -70.8 11.8 5 1 1 1

fos1 2.93 9.4 eman -71.3 -68.3 24 1 1 1

frei -4.36 0.67 prop -62.2 -59.0 25 1 1 1

ftp1 -2.17 3.4 eman -78.9 162.6 15 0 0 1

gmez -0.55 10 eman -73.9 -68.5 24 0 0 1

grw1 -6.98 8.6 eman -62.2 -59.0 25 1 1 1

haa1 3.86 100 eman -77.0 -78.3 1 0 0 1

hugo 0.85 1.29 prop -65.0 -65.7 99 1 1 1

jnsn -3.58 10 eman -73.1 -66.1 24 0 0 1

mait 0.4 1.1 eman -70.8 11.7 5 1 1 1

mant* 30 10 No inf. -74.8 -99.4 22 0 0 1

mar1 7.14 10 eman -64.2 -56.7 25 1 1 1

maw1 -0.35 0.19 cats -67.6 62.9 8 1 1 1

mbl1 2.47 3 eman -78.0 -155.0 19 1 1 1

mbl2 2.28 10 eman -76.3 -144.3 20 0 0 1

mbl3 1.25 17.9 eman -77.3 -141.9 20 0 0 1

mcm4 0.75 0.22 cats -77.8 166.7 15 1 1 1

mirn 24.4 100 eman -66.6 93.0 11 0 0 1

mtcx -3.77 10 eman -78.5 162.5 15 0 0 1

ohg1 4.51 10 eman -63.3 -57.9 25 1 1 1

ohi2 3.36 0.46 cats -63.3 -57.9 25 1 1 1

ohig 4.03 0.67 prop -63.3 -57.9 25 1 1 1

pal1 8.06 10 eman -64.8 -64.0 25 1 1 1

palm 4.83 0.32 cats -64.8 -64.1 25 1 1 1

pig2* 17.9 10 No inf. -74.5 -102.4 22 0 1 1

pra1 4.23 10 eman -62.5 -59.7 25 1 1 1

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prat -9.6 100 eman -62.5 -59.6 25 0 0 1

prtt -5.03 100 eman -62.5 -59.7 25 0 0 1

reyj 151.29 300 eman -62.2 -59.0 25 0 0 1

rob1 5.41 5.1 eman -77.0 163.2 15 1 1 1

robi 8.68 1.54 prop -65.2 -59.4 25 1 1 1

rot1 6.52 10 eman -67.6 -68.1 25 1 1 1

rotb 4.99 0.41 prop -67.6 -68.1 25 1 1 1

roth 5.46 1.35 prop -67.6 -68.1 25 1 1 1

sctb 0.86 0.54 cats -77.8 166.8 15 1 1 1

sig1 22.97 100 eman -60.7 -45.6 26 0 0 1

smr1 0.51 10 eman -68.1 -67.1 25 0 0 1

smrt 1.24 0.85 prop -68.1 -67.1 25 1 1 1

sppt 12.88 100 eman -64.3 -61.1 25 0 0 1

svea 1.28 1.11 prop -74.6 -11.2 4 1 1 1

syog 1.07 0.22 cats -69.0 39.6 7 1 1 1

tnb1 0.1 0.45 cats -74.7 164.1 14 1 1 1

trve -4.57 10 eman -70.0 -67.6 25 1 1 1

ver1 0.33 100 eman -65.2 -64.3 25 0 0 1

ver3 -6.16 100 eman -65.2 -64.3 25 0 0 1

vesl 0.4 0.32 cats -71.7 -2.8 5 1 1 1

vnad 4.35 1.11 prop -65.2 -64.3 25 1 1 1

w01a -0.26 10 eman -87.4 -149.4 17 0 0 1

w01b 1.36 10 eman -87.4 -149.4 17 0 0 1

w02a 0.25 10 eman -85.6 -68.6 2 0 0 1

w02b 2.25 10 eman -85.6 -68.6 2 1 1 1

w03a -1.42 10 eman -81.6 -28.4 3 0 0 1

w03b 1.74 10 eman -81.6 -28.4 3 1 1 1

w04a 3.74 100 eman -82.9 -53.2 2 0 0 1

w05a 2.26 10 eman -80.0 -80.6 1 0 0 1

w05b 7.37 10 eman -80.0 -80.6 1 1 1 1

w06a -2.17 100 eman -79.6 -91.3 1 0 0 1

w07a 3.32 100 eman -80.3 -81.4 1 0 0 1

w08a -1.47 100 eman -75.3 -72.2 24 0 0 1

w09a 2.19 100 eman -82.7 -104.4 18 0 0 1

wasa 0.56 3.2 eman -73.0 -13.4 4 1 1 1

* from Groh et al. 2012 0 not included 1 included Total # 49 50 88

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